Our experimental results show that the proposed method is significantly effective in reducing the feature space compared to the wellknown feature selection methods, and yet the overall effectiveness is similar to or sometimes better than a state-of-the-art approach depending on the PoS of the events.

it refers to the dependent words and their hypernyms in MODifier relation (amod, advmod, partmod, tmod and so on).


(9) sa-mwu-sil ('office) -> sam-sil (10) key-im ('game') -> keym Sentiment analysis or opinion mining techniques that utilize retrieval tasks to obtain the training sets or corpus data have to extract subjective chunks or morphemes from the real-world data.

as the number of the users using social networking services increases rapidly, sentiment analysis or opinion mining capable of automatically extracting the sentiment orientation from online posts has been gaining attention from NLP researchers (Hu and Liu, 2004;Kim and Hovy, 2004;Wiebe, 2000;Pak and Paroubek, 2010).


Summarization of properties in Korean SMS text Ling and Baron (2007) reported that lexical shortening is the one of the most significant characteristics one can see in text messages.

in summary, we will argue that such a Romanization-based retrieval method has several advantages since it provides an easier way to preprocess the data with a variety of linguistic rules.


A Romanization transliteration scheme is used in this study because it naturally represents the phonetic properties of Korean syllables while providing a more intuitive way to apply a set of defined rules to the sequence.

han (2006) applied the transliteration method to perform part-of-speech tagging for Korean texts using Xerox Finite State Tool.


The dictionary-based features are fired if a string in a sentence is registered as a word in a dictionary, and they encode whether the string begins with or ends before the target character, or includes the target character.

although additional time is required to perform the arg max operation, it is practically negligible because the lattice generated in this framework is generally small.


We call this phenomenon substitution with lowercases.

in order to approximate the practical coverage of our method, we classified unknown words that occur more than two times in the Kyoto University and NTT Blog (KNB) corpus 5 into four types: words that are covered by the lexicon created by Murawaki and Kurohashi (2008) (Murawaki's Lexicon), words that are not covered by Murawaki's Lexicon but have entries in Wikipedia, words that are covered only by our method, and the others.


Since long sound symbols and lowercases rarely appear in the lexicon, there are few likely candidates other than the correct analysis.


Most previous work on this approach has aimed at developing a single general-purpose unknown word model.

in addition, as mentioned above, since we Figure 2: Example of a word lattice with new nodes " ," " ," and " ."


The estimated number of negative changes for 100,000 sentences: N * 100kS .

there are few works that focus on certain types of unknown words.


If the new nodes and their costs are plausible, the conventional process for finding the optimal path will select the path with added nodes.


We call this phenomenon substitution with long sound symbols.

in this paper, we introduce derivation rules and onomatopoeia patterns to the unknown word processing in Japanese morphological analysis, and aim to resolve 1) unknown words derived from words in a pre-defined lexicon and 2) unknown onomatopoeias.


(Sun and Uszkoreit, 2012) introduced a Bagging model to effectively combine the outputs of individual systems.

the large-scale unlabeled data we use in our experiments comes from the Chinese Gigaword (LDC2005t14), which is a comprehensive archive of newswire text data that has been acquired over several years by the Linguistic Data Consortium (LDC).


For example, previous work has shown that sequence models alone cannot deal with syntactic ambiguities well (Clark and Curran, 2004;Tsuruoka et al., 2009).

the syntax-free hybrid system is more appealing for many NLP applications.


Identifying the complex predicates has turned to be rewarding.

the semantics of light verbs is, however, kept as such.


In the first experiment, the first sense of each lexical item is selected while in the second, WSD is used to pick the contextually most appropriate sense.

to identify the temporal sense of a numeral, used as nominal, like 2013 is challenging.


As we would expect, we obtain results for WEB TDs that are consistently worse than those in Table 2 (not shown), with one exception: a slight increase for |w| > 8 in email.

for two TDs, tf-idf yields the best result (grp on line 3, BIO on line 24), for four TDs tf (rev, blog, answer, email: line 14).


The identification of non-contiguous LVCs proved to be more difficult for both methods than that of contiguous LVCs.

since different phenomena proved to be difficult for the two systems, a possible direction for future work may be to combine the two approaches in order to minimize prediction errors.


The largest distance between the noun and the verb is 21 tokens and the average distance between the two non-adjacent components is 4.28 tokens.

it was difficult for both the dependency parser and the classifier to recognize rare LVCs or those that included a nonfrequent light verb.


In several NLP applications like information retrieval or machine translation it is important to identify LVCs in context since they require special treatment, particularly because of their semantic features.

uralom alá jut rule under get "to get under rule" or hatás alattáll effect under stand "to be under effect").


Note the increase in FRecall up to rank @∞ for known ASR and unknown-OOV Text and ASR, which indicates that correct interpretations are returned at very high ranks when input words are not identified (NDCG increases only modestly, as it penalizes high ranks).

2 These situations, which affect the performance of an SLU system, are characterized along the following two dimensions: accuracy and knowledge.


However, this mode of evaluation, which we call Generative, does not address whether a system's interpretations are plausible (even if they are wrong).

in the next section, we discuss related work, and in Section 3, we outline our system Scusi?.


's "punitive" at-titude to attributes that do not match reality, such as a bookcase not being under any portrait, may need to be moderated.

notFound@K counts the number of representable utterances for which no correct interpretation was found within rank K. notFound@∞ considers all the interpretations returned by an SLU system.


Our study had 26 participants, who generated a total of 432 spoken descriptions (average length was 10 words, median 8, and the longest description had 21 words).

descriptive prepositional phrases starting with "with" or "of" may be judiciously ignored, or the referent may be disambiguated by asking a clarification question.


If they match, no replacement is performed.

the second stage applies Charniak's probabilistic parser (http://bllip.cs.brown.edu/ resources.shtml#software) to syntactically analyze the texts in order of their probability, yielding at most 50 different parse trees per text.


In both cases, the probabilities assigned to the labels by the classifier are moderated by the classifier's accuracy.

both Scusi?+N+P+R and Scusi?+P+R outperform the original version of  Average of Scusi?


One obvious benefit of triangulation is to increase the coverage of the model on the input text.

a tuning procedure should assign higher weights to the models that produce higher quality translations and lower weights to weak models in order to control their noise propagation in the ensemble.


For our experiments, we used the Europarl corpus (v7) (Koehn, 2005) for training sets and ACL/WMT 2005 1 data for dev/test sets (2k sentence pairs) following Cohn and Lapata (2007).

instead of using only the best translation, they took the n-best translations and translated them into the target language.


The composition of this data is shown in Table 1.

the most effective settings were to use Ex-tWeight on a sentence level context.


Confidence estimation systems usually do not have gold standard data and are mostly a linear interpolation of a large group of scores.

these features consist of the average of the sentence-level features described above.


L 1 regularization method (Tsuruoka et al., 2009)  model with the +id feature setting on both the development set and training data set, respectively, and their hyperameters are tuned on the dev-test set.

experiments on IWSLT Chineseto-english translation tasks show that, with the help of grouping these features, our method can overcome the above pitfalls and thus achieves significant improvements.


Then, it begins to calculate W initialized as 0 and G .

in the first step, we learn a group structure of atomic features in the large training data for better coverage.


Our ASR system is a five-pass system based on the open-source CMU Sphinx toolkit 3 (version 3 and 4), similar to the LIUM'08 French ASR system described in (Deléglise et al., 2009).

this transcription is then split into phrases and translated by a baseline SMt system into language L2.


Our multimodal comparable corpus consists of spoken talks in English (audio) and written texts in French.

we filter the selected sentences or phrases in each condition with different TER thresholds ranging from 0 to 100 by steps of 10.


For the English data, NEs are limited to those beginning with English characters and consisting of only English characters and some specific symbols ( .− :, !#).

in this section, we design collaborative learning mechanism, which contains inter-class and intraclass scoring criteria, to better control the quality of the patterns and NE instances bootstrapped in iterations.


To improve the accuracy of wrappers, a lot of constraints such as part-of-speech tags (Etzioni et al., 2005) and trigger words (Talukdar et al., 2006) were introduced to tackle the tricky conditions.

likewise, if a pattern of class c i can also be generated by seeds from other categories, this pattern is obviously not a high-quality pattern for category c i .


The samples with different annotations are then reviewed by both annotators to produce the final result.

precision is defined as the percentage of correct NEs of a given class from the automatically extracted ones.


The experimental result of Twevent (precision 75.7%) is lower than that reported by Li et al.

(2012b) for an named entity recognition system on Twitter.


(2012) tried to generate queries for a planned event to relax the limitation.

4249 event clusters in Gset were manually labeled into 804 news events and 3445 nonevents.


make a mistake), a literal verb + noun combination (e.g.


The results published in Tu and Roth (2011) are good on the positive class with an F-score of 75.36 but the worst with an F-score of 56.41 on the negative class.


Since the syntactic and the semantic head of the construction are not the same, they require special treatment when parsing.

in the second approach, the goal is to detect individual LVC token instances in a running text, taking contextual information into account (Diab and Bhutada, 2009;Tu and Roth, 2011;.


Most of the above criteria are consistent with the original setup of collecting the truthful reviews in the op spam v1.3 dataset.

baseline features: 3,009 unigrams, 10,538 bigrams, and 6,571 syntactic production rules, as described in Section 5.1.


obtained their best performance using unigrams alone, together with deep syntax features.

s a i is computed as the cosine similarity between D i and D i .


With respect to the op spam v1.3 dataset used in our work, human judges were only able to achieve 60% accuracy, and the inter-annotator agreement was low: 0.11 computed by Fleiss's kappa .

much previous work on detecting deceptive opinions usually relies on some meta-information, such as the IP address of the reviewer or the average rating of the product, rather than the actual content of the review Liu (2012).


Intuitively, in order to define a new epoch, both a big social impact of a series of events and new issues, which arouse the social interest, must be observed.

we investigated the significant changes in the distribution of terms in the Google N-gram corpus and their relationships with emotion words.


If Least Square is positive then also the others are positive as well.

there is no the epoch distinction and statistical support.


As shown in Table 3, the average length for COMMENTS is only 10.5 words, on par with TWITTER-1/2 (but according to this evidence, more carefully constructed).

in Table 4 we show the results of parsing 4000 randomly selected English sentences from each corpus using the ERG with the parsing setup we have described.


This means that it is possible that some of the sentences have been spuriously identified as grammatical, since the very general types for unknown words give the grammar great flexibility in fitting a parse tree to the sentence, even where it may not be appropriate.

in the longer sentences of FORUMS and BLOGS, there is more scope for the authors to introduce anomalies into the text, increasing the chances of the sentence being unparseable.


The overall best performing features were WD, QD, MP and OIT, which is in line with the findings in the correlation study in Section 5.2.

many interactions happen outside the context of a pre-defined static power structure or hierarchy.


They can be called scalar uncertainties since in both cases, a scale is involved in the interpretation of the uncertain term.

although they are similar, we suggest that peacocks and hedges be differentiated in our classification because peacocks are related to subjectivity while hedges are more neutral, hence they can be relevant for different NLP applications (e.g.


Associated software is to be given to help reconstruct tweets using their IDs.


However, the canonical word order in Tunisian verbal sentences is SVO (Subject-Verb-Object) (Baccouche, 2003).

the word order is generally reversed when passing to tD.


For example, for the root رج :خ xrj, we can apply different patterns which give different lemmas with different meanings: َج ر ْ خ َ أ / to eject 3-Lemma: The lemma is a fundamental concept in the processing of texts in at least some languages.

we can distinguish in the same speech, MSA words, TD words and MSA-TD words such as a word with an MSA component (root) and dialectal affixes.


Nowadays in tunisia, the arabic Tunisian Dialect (TD) has become progressively used in interviews, news and debate programs instead of Modern Standard Arabic (MSA).

the canonical word order in Tunisian verbal sentences is SVO (Subject-Verb-Object) (Baccouche, 2003).


We label the features as simple, progressive and perfect.

it is hence important to produce the right inflections and auxiliary verbs.


(2012) use small datasets with fewer than 100K sentence pairs.

we also observe similar trend for several source phrases in both Arabic-English and Chinese-English corpora.


• Faster decoding: The compact grammars naturally result in faster decoding and we observed up to 20-30% speedup in the translation including the time spent for loading the model.

we use the Q-Q plot to study the behaviour of two probability distributions as explained below considering the Chinese phrase 联 合国 (united nations) as a representative example.


This is because the additional corpus expand the diversity of the topic model, especially for NIST08 which contains a large part of web data, generating more accurate topic distribution.

we integrate our LM into SMT system to utilize topic distribution of the test-document as a trigger to each topic-specific language model.


We first estimate the topic distribution for each document in training data, and assign those topic probabilities to each sentence, then, we train a topic-specific n-gram LM for each topic based on those topic probabilities.

the "bag-of-words" assumption is an unrealistic oversimplification in language model case because it ignores the order of words which is critical in estimating n-gram probabilities.


Reserve a random portion R of L, and the remaining set L = L − R is used for training.

figure 2 shows the performance of supervised polarity classifiers for 20 topics based on different imbalanced classification methods.


Illustrated in Figure 1, it can be observed that all the classes of training corpus are biased.

finally, section 6 summarizes the work, draws some conclusions, and suggests related future work.


Eight sentiment resources (see Table 1) were standardized to generate eight sentiment diction-aries (D j , j=1,…,d).

implications for utilizing multiple dictionaries are discussed.


Finally we include our proposed Balanced Proximity-based Model, denoted as BPM, which formulates semantic proximity and positional proximity into a unified language smoothing framework, with flexible intra-document smoothing and inter-document smoothing.

pLM mainly utilizes positional information while no semantic association is considered.


p (w|i, d) is the language model at position i.

to control the impact of out-ofdocument vocabulary, we add a parameter µ ∈ [0, +∞) here: Analogously, for the positional-based smoothing, the smoothed count by positional proximity is We apply the best positional proximity based density function of Gaussian projection ψ(i, j) in (Lv and Zhai, 2009).


In each experiment, we first use the baseline model (KL-divergence) to retrieve 2,000 documents for each query, and then use the smoothing methods (or a baseline method) to re-rank them.

to the simple strategy which smoothes all documents with the same background, recently corpus structures have been exploited for more accurate smoothing.


So, the general probability can be simplified to: where h k is a lexical word, h k+1 is the head word of h k , and o k ∈ {⟨L⟩, ⟨R⟩} is retained in the history to show the dependency orientation.

we further propose an expectation-maximization (EM) algorithm for estimating the probability of arbitrary order 3 dependency N-grams, by considering all possible dependency structures 4 of a sentence (Fig.


The convergence trend along with the iteration times can be observed.

we collect from the raw corpora all possible lexical dependency Ngrams 15 without any cut-off thresholds for models of every order.


, n}) can be done by taking the gradients of α i with respect to the training objective and directly plugging it into a gradientbased optimization framework, such as the limited memory variable metric (LMVM) used by Smith et al.

unlike its more popular sibling 1 2 norm (used in group lasso), which seeks feature sparsity at the group-level, 2 1 norm encourages sparsity within feature groups.


The latter becomes an issue where input texts are not fully known or come from different sources as in the web.

one way to evaluate our approach is to compare it to each pipeline In practice, relying on such a fixed pipeline involves the danger of choosing a slow one.


Online adaptation always did better than the random baseline but not than the optimal baseline except for training size 1.

we employed the UIMA tokenizer 2 to generate tokens and sentences, and the TreeTagger for part-of-speech tagging and chunking (Schmid, 1995).


van Noord (2009) trades parsing efficiency for parsing effectiveness by learning a heuristic filtering of useful parses.

due to the algorithms' input constraints, however, k is normally much lower in practice.


Supervised methods recast keyphrase extraction as a binary classification task (Witten et al., 1999), whereas unsupervised methods apply different kinds of techniques such as language modeling (Tomokiyo and Hurst, 2003), clustering (Liu et al., 2009) or graph-based ranking (Mihalcea and Tarau, 2004).

the completeness of the graph has the benefit of providing a more exhaustive view of the relations between topics.


The conditional random fields CRFs (Lafferty et al., 2001) have shown empirical successes in label sequence labelling problem.

the example shown in Figure 2 clearly depicts the way features are mapped from a tree structure intent representation of an NL query.


Some cases of cyberbullying lead the students who were bullied to assault or kill themselves or the student who wrote the bullying entry on the BBS.


We used the terms shown in Table 11 to detect such expressions.

as shown, there are many cases where the analyzer should not propagate due to scope, and there are also many cases where the analyzer should propagate as −.


Coverage for the dictionary of Japanese functional expressions also becomes a problem.

for instance, PR is interpreted by probable and PS is interpreted by possible.


In the same way, an event "kaet" (go home) in (1b) is labeled with PR+ and "hassei-suru" (occurrence) in (1c) is labeled with CT−.

in our experiment, we focus on the propagation, so we do not use gold contextual factuality.


To avoid the randomness in data, we adopt five-fold cross-validation on it.

the other two baseline approaches are listed as follows: Lin's similarity (Lin, 1998) (LIN98).


This is because it is difficult to assign an absolute score of semantic similarity to a pair of words, especially when seasoned linguists are not available.

similar to ds P M I 3wd (•, •) based FUsE, performance of LIN98 and JCs drops while shrinking the number of relation instances.


In future studies, we will experiment on more elaborated combination similarity fusion mechanisms other that linear combination.

we introduce a semantics layer upon the distribution layer to exploit semantic relations.


Such phenomena usually flip the entailment relation.

we adapt the original algorithm from two aspects.


Because break points are the boundaries of words, we first collect the known words in the corpus, and take their boundary points as the positive samples.

a total of 56 features, in the highest frequency word dataset with 6gram learning samples, were calculated and sorted by the f-score algorithm proposed by Chen and Lin's SVM feature-selected research (Y.-W. Chen & Lin, 2006).


7 For each percentage, the result of random selection are the average of three runs of random selection.

we would also like to evaluate our approach on other NLP tasks, and test its performance with other machine learning algorithms.


(e.g., 10%, 20%) of source-domain data is selected, the OOV rate of the test data is much lower when Cov is used.

when feature augmentation on CWS has higher improvement, usually it also brings higher improvement on POS tagging when comparing across different test data (e.g., the improvement on BC is higher than NW for CWS, and the same is true for POS tagging).


We thus needed evaluation measures to decide about the quality of a cluster analysis.

mass across the classes of which it is a member.


As an auxiliary analysis we also extract the converged scores of prosody nodes and rank them in order to analyze their effectiveness.

a second variant of this baseline selects the utterances that appear in the beginning of the document.


The rest of the paper is organized as follows: we discuss the sentiment analysis of healthrelated online messages, then we introduce our data; next we discuss the Subjectivity Lexicon and the features we use to represent the data, the analysis of the manual annotation and the machine learning classification results, before we conclude the presentation.

out of these 21, only 10 words were found unique with their part of speech.


Users express their sentiments differently on forums compared to the way they express opinions when providing reviews or sharing messages on social networks.

the performance has increased by 4.2% on average among the three classifiers.


Using H(n), the probability distribution of the network can be represented as P(n) = exp{−H(n)}/Z, where Z is a normalization factor.

fixed seed words are used with different β values.


Finally, the test set contains 63 songs written by five non-suicide artists and 46 songs written by 4 suicide artists.

in our search for lyricists who committed suicide, we looked for lyricists who met the following prerequisite: the suicide had to be relatively unambiguous.


(2011) and Stirman & Pennebaker (2001), we expected to find differences in the use of the passive constuction and in the proportions of the first-person pronouns to the rest of the pronouns.

even if we figured out a way to split the corpus into sets of the appropriate numbers of songs while taking into consideration that the artists for each set must be unique, there are further factors that could skew the results.


The specific morpho-syntactic behaviour of the kan-affixed verb is very much determined by the type of stem it attaches to, and its resulting behaviour varies from stem type to stem type (Kroeger, 2007;Vamarasi, 1999;Arka, 1993).

we use pairwise precision (pP ), recall (pR), and F-score (pF 1 ) to evaluate our generated clusters, relative to the gold-standard word classes, as described by Schulte im walde (2006).


For our case study, we aim to discover groups of like stems that, when used predicatively in the same morphological context, give rise to the same syntactic behaviour.

lapata and Brew (2004) develop a semi-supervised system that generates, for a given verb and its syntactic frame, a probability distribution over the levin verb classes.


The TYPES-HDP system, on the other hand, barely exceeds the Majority Class baseline with the ON-ALL experiment, and fails to do so with the ON-VERBS experiment.

we aim to induce classes of stems that exhibit the same syntactico-semantic behaviour when they have the same morphological marking.


When the number of instances is large enough, the statistical model will effectively incorporate these entity type constraints as long as entity types are extracted as features.

we can consider further efforts to enlarge and balance the initial set from the view of non-relation approximation.


To use multiple parse trees for a single path, we also developed a linear classification model whose output score approximates the difference between the log probabilities of the path being derived from positive and negative relations.

first, the last tokens of sequences, namely arguments, are dropped, because of the observation that this makes it easy to convert the components of patterns into sequences and their subsequences in a systematic way.


This work was supported by the National Research Foundation (NRF) of Korea funded by the Ministry of Education, Science and Technology (MEST) (No.

the independence constraint may not capture the nature of dependency paths, but makes it cheaper to learn lexical rules.


They find that the use of such generalized sequences improves the performance of the task of identifying opinions from product reviews.

we find that the use of PCFGs learned by our pseudo-count method improves the performance of classifiers in a statistically significant manner, compared to a baseline classifier with ngrams encoding partial structures of paths.


One possible explanation is that the wider the beam is the more erroneous parse trees are likely to affect the final decision of classifiers.

likewise, the biological event extraction research, a branch of information extraction, stresses the importance of the role of dependency paths in identifying event-argument relations due to the resemblance of eventargument relations to dependency relations (Björne et al., 2008).


For this reason, the lower bound ( ) of ( ) is: Instead of ( ), we use ( ) at risk of the deterioration of the performance of the resulting model, since it is apparently easier to handle than ( ).

the core component alone can be used to detect the THEMEs of positive regulation events (e.g., "IFN-induced IP-10"), but the subordinate component alone cannot.


The edge weights are determined by various factors such as semantic similarity or syntactic similarity between nodes.

for each phrase p, we extract three vectors !


"in a nutshell " "on the whole" "generally speaking" "in general" "in brief" "broadly speaking" CP q Once the factors are selected, we have to determine the weights of the factors, (i.e., !

we train the weights of factors such that the performance is optimal for a given developing data set.


For each intervention appearing in a sentence, we identify all the terms that are connected to it via specific dependency chains using the following rule: 1.

for the preliminary annotation and analysis, we used the same data as the task-oriented coverage analysis work described in (Sarker et al., 2012).


This is because, IC only relies on the cosine similarity between textual features of tweets to form clusters, while IC-Time enforces a time constraint in the similarity measure to reflect the time locality of events, which thus leads to better clustering accuracy.

for this purpose, we design an efficient single-pass clustering algorithm which clusters the stream of tweets in an incremental manner.


(1) Remove leaf nodes which are not link nodes; (2) If the node is the only child of of its parent node, delete the parent node and directly connect the node with its ancestor node; (3) If the node has two children nodes, and the first child node is the link node, while the other is not, then delete the node and connect the two children nodes with its ancestor node.

classification and clustering: Expected cross Entropy is selected for feature selection.


In addition, as the vertex number increases to a value, the running time of searching for the maximum subgraphs becomes unacceptable.

libxml2 is used to parse the web pages into the DOM trees.


We observed that our Ranking algorithm Rel-Div performs at par with (and sometimes even better than) M-Div and M-Div-NI.

a part of the graph for the query "sun" is depicted in Figure 1 Sun light  In order to build a learning model for b q , it is important to define a good set of features that characterize the node's relevance to the query.


• Empirical study: We report on our investigation of applying supervised learning algorithms and leveraging different feature representations, whose results will be used as a foundation for a larger case-based reasoning approach.

for example, if a user types "!flash", then the bot would output "To install flash see https://help.ubuntu....mats/flash -See also !Restricted and !Gnash".


We describe an approach to automatically distinguish bot-answerable questions, which would mitigate this problem.

f 0.5 is more appropriate here than the standard f score because it places more emphasis on precision.


The differences between the correlations are statistically significant at p < 0.05 (using Student's t-test) except for the differences between the LSA+SYN system and the baseline, and between the LSA+SYN+SEM system and the LSA+SEM system.

the length of the essays is another issue since longer essays tend to capture more information in their representative vectors which provides the scope for a better similarity matching with the semantic space.


The line CH in Figure 1 shows the definition of chunks.

table 7 shows the error changes when different features are added.


Table 4 shows the Chinese SRL results after adding the N-best dependency parsing related features.

we do not explain "standard" features, however, we give a detailed description of the features used in this work.


Their method relies on the synset-category mappings of (Ponzetto and Navigli, 2009), extending it with information obtained from the hyperlink structure of the Wikipedia articles.

there have been many attempts to extend Word-Net with concepts from Wikipedia.


Based on our linguistic analysis, we formalize a set of temporal elements that are the blocks used to construct time entities, such as century, year, month, day, hour etc.

tempEval-2 corpus includes training and test parts, as shown in table 2 We analyse the annotation scheme based on training data and then add some additional rules on durations, such as shinian (ten years), shi-tian (ten days), and some approximate expressions, e.g.


the min-guo period established in 1912 after Qing dynasty.

we use two different corpora: Sinica (Chen et al., 1996) and TempEval-2 from SemEval-2010 competition (Pustejovsky and Verhagen., 2009).


The notation * denotes that except the term "concern" there are other terms that occur only one time among 6 ranking models, which are listed as follows: breach, profit, violat, regain, uncomplet, accid, abl, integr, doubt, grantor; similarly, for the notation ∧, the terms are: incorrectli, fault, nondisclosur, misus, breakag, defalc, excit, unclear, sentenc, overdu, omit, inforc, irrevoc, unencumb, further, variant, precipit, libel, loss.

in the following discussions, we conduct some analyses on the words learned from the ranking models.


But these (Pradhan et al., 2012;Ng, 2010; are mainy in non-Indian languages.

we train CRF with the following set of features.


For training and development datasets, anaphoric annotations were provided by the organizers.

the classifier has to decide, given the features, whether the anaphor and the candidate antecedent are coreferent or not.


(1997), , Bejan and Harabagiu (2010), Chen et al.

here, we show one ordering in which the parent(s) of a component are considered after the component itself.


For that reason, a number of recent research papers have focused on these questions via analyzing the inner workings of entity coreference resolvers (e.g., Stoyanov et al.

no attempts have been made to address these questions in the context of event coreference.


Indeed, previous studies indicate that over 70% of all queries contain entities (Guo et al., 2009;Yin and Shah, 2010).

the confusion matrix for NERQ-2S shows that errors, basically, regard highly ambiguous terms.


Consequently, Cabocha, which takes words/characters and their POS tags as features for discriminative training using a SVM model (Kudo  and Matsumoto, 2002b), can still correctly tend to include these single-Kanji-character words into one chunk.

abbreviations, food names and event names are formed and shared in Twitter and Facebook.


In order to increase the chance that the first retrieved web pages contain the possible answers, we apply a novel query expansion approach using answer patterns.

our base question answering module uses the given question phrase as a search engine query.


One keyword missing may lead the change of the tweet's classification result.

constructing such a classifier is a challenging task, as tweets are short and informal.


Reflecting the rapid growth in the use of opinionated texts on the Web, such as customer reviews, opinion mining has been explored to facilitate utilizing opinions mainly for improving products and decision-making purposes.

because this difference is not important in our research, we usually use the term "evaluation".


However, even a list of 1,000 cognates is a hard constraint for some language pairs.

in a follow-up experiment, we use the full size of each training set.


Evaluation Metrics In order to estimate the cognate production quality without having to rely on repeated human judgment, we evaluate COP against a list of known cognates.

in the remainder of the paper, we refer to our method as COP (COgnate Production).


Especially the production of Farsi cognates works very well, although the training data has not been filtered.

an alternative approach is to manually extract or learn production rules that reflect the regularities (Gomes and Pereira Lopes, 2011;Schulz et al., 2004).


In order to filter the resulting list of words, we transliterate Russian and Greek into the Latin alphabet 11 and apply a string similarity filter.

a student is more likely to understand a word if there is a similar word in a language she already knows (Ringbom, 1992).


The classification accuracy on f Lex ,f Lex + f Syn and f Lex + f Syn + f Sem features have been increased by 1.36%, 2.74% and 0.69% respectively.

first, that is the only standard taxonomy that exists in Bengali QC so far.


Depending on the overall reject rate, our system could get a significant increase in the complete recognition time while the returned accuracy of our system is promising compared to that of the single SVM classifier.

in other experiments using less training data as presented in table 4, we trained the SVM classifier based on the combination of DiffPosNeg and BoW features.


Although NBs are very fast classifiers requiring a small amount training data, there is a loss of accuracy due to the NBs' conditional independence assumption.

remaining documents, that are detected as "hard to be correctly classified" by the NB classifier in the use of rejection decision, are forwarded to process in a SVM classifier at the second stage, where the hard documents are represented by additional bag-of-words and topic-based features.


This improvement is slight as the amount of clicked Wikipedia links is small with respect to the whole collection.

answers, if the search query is close to a Qa-system like question.


In order to fulfill the missing relationships at phrasal category level, a mapping work of phrasal tags for Chinese (Zh), English (En), French (Fr), German (De), and Portuguese (Pt) is presented.

in addition, glue rules are added in allowing combinations of partial translation fragments monotonically.


In particular, we observed that the parsing accuracy (either on the original or universal tag-set) for Chinese language is lower compared   with other languages, which possibly led to poorer alignment relationships at tree level.

it consists of twelve different tags, including: NOUN (noun), VERB (verb), ADJ (adjective), ADV (adverb), PRON (pronoun), DET (determiner and article), ADP (preposition and postposition), NUM (numeral), CONJ (conjunction), PRT (particle), "."


We used a 5-gram language model for all the languages based on the SRILM toolkit (Stolcke, 2002).

syntactic information is being integrated either on the source or target or both side(s) in training translation models for handling the translation task.


Among all, PR+tfidf achieves the best performance.

in each iteration, a word's interestingness score is the linear combination of its interest preference score and the sum of the propagation of its inbound words' previous PageRank scores.


A different semantic of one word has a different DEF description.

based on these, we will optimize parameters for various applications.


→ The court deemed it necessary that she respond to the summons.

preposition at has been replaced by in before a place like at central London.


From these two rules, the grammar extractor wont be able to derive hate → hates.

grammar correction can be considered a translation problem from incorrect text to correct text.


The results show that, as expected, the performance of the parser over this corpus decreases, making even more difficult the extraction of SCFs.

the treebanks used in this work contain up to 25 different dependency relations.


A particularity of this approach is that, to enable the comparison of context vectors, it requires the existence of a seed bilingual dictionary to translate source context vectors.

in the financial domain, translating action into deed or lawsuit would probably introduce noise in context vectors.


We discuss the learning for the POMDP that uses our IDG.


Then we keep these words as the nodes in bilingual derivation trees, and continue to generate parent nodes by combining these child nodes bottomup.

the sizes of these corpora are listed in table 1.


In the baseline system, translation model (LDC) was trained on the LDC corpora that had been cleaned and thought to be less noisy.

we cannot always get the translation performance improved by simply enlarging our training data.


We propose a per field EM formulation for finding the importance of the expansion terms, in line with traditional PRF.

hence, the T itle field has a larger impact as compared to the Body field.


One complication is that the question text may have more than one sentence with a question mark after it-in fact, each thread contains 2.2 sentences ending with question marks, on average.

we are interested in learning a reranking model that is generally applicable to question answering systems.


For example, the future prefix ,سـ( s, "I will") is transformed in TD to ( ,با bA$, "I will").

we calculate the number of words for which the analyzer attributes at least one correct analysis.


Thus, on the P&N model, the average conditional entropy per feature given the class (how surprising the feature is when we know the answer) increases by 8.8% when the oracle is unavailable.

in all of the feature sets we see a marked drop moving from micro-average (average over instances) to macroaverage (average over connective types)-P&N, for instance, goes from 93.0% to 85.3%.


These errors percolate leading to erroneous text-level discourse processing.

on the P&N model, the average conditional entropy per feature given the class (how surprising the feature is when we know the answer) increases by 8.8% when the oracle is unavailable.


Rare terms that occur only in δ documents or fewer are eliminated by replacing them with the average vectors of the documents that contain them.

assuming amortized constant time for set testing and insertion (Sedgewick, 2002), that the update is smaller than the corpus, i.e., U < D , and that the number of documents is greater than the number of terms, i.e., n > m, the update algorithm can be executed with a complexity of O(( D + U ) nnz k + mk ), where nnz is the expected number of non-zero elements in any document vector.


We introduce an effective batch updating algorithm, and present performance measurements on a subset of the Reuters newswire corpus that show that a 12.5% to 50% split of the documents into corpus and update vectors leads to a three to four fold speedup over a complete rebuild.

for deletions, we let denote  index permutation to remove demoted terms e i i-th standard base We associate every update u = (i, v) ∈ U with two term frequency vectors.


The models are compared with a baseline system and two versions of the PBA wherever relevant.

all possible adddelete rules are applied on a given word form and the resulting lemma is checked against the vocabulary to find if it is right or not.


Morphological analysis is the task of analyzing the structure of morphemes in a word and is generally a prelude to further complex NLP tasks such as parsing, machine translation, semantic analysis etc.

evaluation is performed on individual attributes as well as on the combined output.


This is an iterative process starting with the initial score, S 0 , calculated using frequency counts (as in section 4.1).

some languages (in particular Semitic languages) have another type of word formation in which morphemes combine in a non-concatenative manner, through the interdigitation of a root morpheme with an affix or pattern template.


If their relation phrases and first arguments are matching, the mismatch of second arguments will be calculated.

this rule is not applied for "isA" (equivalent) relations.


Word alignment approaches are generally used to construct bilingual lexicons automatically from parallel corpora.

the obtained results show that the inclusion in the training corpus of word alignment results integrating transliteration has improved the translation BLEU score from 20.15 to 20.63 (a gain of 0.48 points).


This step uses the results of the transliteration into Latin script of all the proper names present in the Arabic corpus and can identify, for example, that the proper name "Kosovo" and the transliteration of the Arabic word " "آ ("kosoufou") are cognates.

in section 3, we present briefly the system for automatic transliteration of proper names from Arabic to Latin script.


This approach has a strong limitation when used in word alignment as it proposes only one transliteration for a given name.

this step does not detect pairs of words such as "Algeria" and "aljazair" (transliteration of the Arabic word " ا .


Furthermore, we construct a weighted directed graph by matching the tree of the original sentence with semantic trees of each sense candidate.

(3) The basic principle is to count C 1,1 (the number of occurrences of w i and w j together), C 1,2 ( the number of occurrences of w i without w j ), C 2,1 (the number of occurrence of w j without w i ) and C 2,2 (the number of bigrams in the corpus that don't contains w i or w j ).


Ideally, the POS tag of the parent would be a sufficient feature because reflexives in the second meaning should depend on a noun.

the specialized models have been integrated in the tectoMt system and extrinsically evaluated on the English-Czech test set for the WMt 2011translation task (Callison-Burch et al., 2011 this data set contains 3,003 English sentences with one Czech reference translation, out of which 430 contain at least one occurrence of it and 52 contain a reflexive pronoun.


This method gives preferences to sub phrases according to their length and score.

w +r }: of the work in Le and Shimazu (2004) we choose such collocations that their lengths (including the target words) are less than or equal to 4, it means (l + r + 1) ≤ 4.


and Processing Pipeline The CDEF data structure is defined based on widely used de-facto standards such as TEI (Vanhoutte, 2004), CES (Ide, 2000), and common interface format being developed under the context of ISO committee TC 37/SC 4 (Ide, 2009b).

this paper realizes interoperability between those two types of frameworks to mutually complement their disadvantages.


Comparative experiments show that our approach could efficiently reduce feature dimensionality and enhance the final F 1 value.

experiments and analysis are arranged in Section 4.


Natural language data is implicitly richly structured, and making use of that structure can be valuable in a wide variety of NLP tasks.

in future work, we plan to apply our algorithms to a wider range of tasks, and we will present an analysis of the properties of online learning algorithms over latent structures.


For example, for the query term (bulb, valve), the proposed method uses only the translation "valve".

their work defines the context of word w in a certain sentence, as the translation of word w, in the corresponding translated sentence.


6 In the first experiment we assume that the user wants to acquire all synonyms irrespectively of the difference in senses.

for example, in the automobile domain, the ambiguous Japanese word (bulb, valve) has the synonyms (bulb) or (valve), depending on the meaning intended by the user.


Given a text snippet in which the ambiguous word occurs, their methods select the appropriate sense by finding an appropriate translation.

as a bilingual dictionary, we use a largesized Japanese-English dictionary with 1.6 million entries.


Unlike the constituency systems, the system with higher-accuracy parses performs better.

this shows that tree kernels still require the regularity encoded in the lower-and higher-accuracy trees.


The experiments show that, comparing to the baseline method which does not utilize sitelevel knowledge, our method can improve the extraction performance significantly.

we evaluate on two aspects where site-level knowledge takes effect, they are: 1) weak semiblock identification in page-level extraction; 2) AV pair extraction in page-level extraction.


Disputed document Q is then compared to each author profile A k using a metric D(Q, A k ) and is attributed to that author for whom D is minimum.

based method we choose terms based on their term frequencies in the corpus.


Concept of capitalization does not exist as such in Gujarati language.

for given query text Q and author profiles A k The distance function D depends on the method used and is defined separately for each method and so is true for the author profile A k .


we evaluated our method in terms of accuracy on two real-world datasets (Blitzer et al., 2007;Maas et al., 2011) for a document-level sentiment classification task.


They then classify a given review by referring to ratings given for the same product by other users who are similar to the user in question.

the datasets contain user/product and only product information.


the suffix comparison relies on the dictionary, D: if the two strings have matching suffixes in the end according to the dictionary, it returns the length of the suffixes, which is received by a and b in the modified algorithm.


Nowadays, the widespread use of Unicode is one of the reasons of text variant, particularly when it comes to text processing, because many NLP tools, e.g., syntactic parsers, cannot handle Unicode characters properly.

while those annotations may serve their goals individually, further benefit, e.g., reuse, comparison, or aggregation, can be gained from interoperable use of them.


Thus, NBC with ULM usually suffers from the issue of word usage diversity and polysemy, which can degrade the DC performance.

section 2 defines the notations and briefly reviews the related work.


The process dimension of CIGs consists of four pillars: (1) activities to be executed; (2) the resources they use or consume; (3) the actors that execute them; (4) control flows and gates that temporally constrain activities.

the intuition behind these features is that certain types may correlate strongly with syntax (e.g., one would expect "resource" to annotate an object NP).


Considering the cost of consistency both the ranking results in initial f 1 and the feedback from f 2 , we define the cost function caused by refining f 1 with f 2 in the (t + 1) th round iteration as where f 1 denotes the initial ranking scores of f 1 .

formally, given a set of candidate templates T S candidate = {T (1) , T (2) , ..., T (n) } ⊂ R m , let f k : T S candidate → R denote the ranking function on the k th feature, where f k ∈ f = {f 1 , f 2 , f 3 }.


Our proposed method extracted accurate parallel fragments, which led to correct new phrases.

they extract sub-sentential parallel fragments by using a Log-Likelihood-Ratio (LLR) lexicon estimated on an external parallel corpus and a smoothing filter.


Hewavitharana and Vogel (2011) propose a method that calculates both the inside and outside probabilities for fragments in a comparable sentence pair, and show that the context of the sentence helps fragment extraction.

the baseline system used the parallel corpus (680k sentences).


Note that exact match criteria has a bias against (Munteanu and Marcu, 2006), because their method extacts subsentential fragments which are quite long.

the noise contained in the data extracted by "+sentences" and "Munteanu+, 2006" produced many noisy phrase pairs, which may decrease MT performance.


Stacked generalization is a general method of using a meta-level model to combine base-level models to achieve higher accuracy.

most ensemble methods use a single base learning algorithm to produce homogeneous base learners, but there are also some methods which use multiple learning algorithms to produce heterogeneous learners.


At the end of this step, 5 different systems are built based on 5 different training sets, called 5 ,..., 1 , = j SMT j .

this algorithm is introduced for combining multiple models, we focus our attention to utilize this algorithm in order to improve only a single SMT system.


The general strategy is to propose a number of sets of biterminal rules and a place to segment them, estimate the posterior probability given these sets and commit to the best.

we introduce a minimalist, unsupervised learning model that induces relatively clean, compact phrasal translation lexicons by employing a novel Bayesian approach that attempts to find the maximum a posteriori (MAP) or minimum description length (MDL) model.


The transduction grammar that fits the training data the best is the one where the start symbol rewrites to the full sentence pairs that it has to generate.

we directly evaluate the grammars that we induce.


We can see that selecting features within individual feature types leads to better results compared to applying feature selection to the full set.

quality Estimation (qE) involves judging the correctness of a system output given an input without any output reference.


For Persian-English translation model, weights are optimized using a set 1000 sentences randomly sampled from the parallel corpus while the English-Arabic translation model weights are optimized using a set of 500 sentences from the 2004 NIST MT evaluation test set (MT04).

we show positive results for Persian-Arabic SMT (from 0.4 to 3.1 BLEU on different direct training corpus sizes).


e is the English pivot phrase that is common in both Persian-English translation model and English-Arabic translation model.

work on Persian SMT is limited to few studies.


The English side is tokenized using Tree Tagger (Schmid, 1994).

recent research (Carpuat and Diab 2010, Bouamor et al., 2012 show that explicitly modeling MWEs in the SMT framework yields non-negligible gains depending on the integration method.


In our application scenario, we plan to implement several different text manipulation methods (and combine them based on their confidence).

this manipulation affects the watermark bit contained in the passage and thus allows information encoding.


this at the cost of relatively low precision (around 70% even when using the information about the correct word sense).

these results suggest that our methodology, making a change in 7-14% of the sentences with a precision of 90-98% is a very competitive alternative for precision-oriented linguistic steganography.


A good overview and comparison of different statistical approaches is given in Farkas et al.


With the help of the framework we preliminarily propose a sequence cutting strategy to enhance the current IME.

we annotate it with pinyin sequence using the method in (Yang et al., 2012b).


Using these results, we conclude that the network predicted by our system that uses tree kernels performs well in terms of extracting an unweighted, undirected network from Alice in Wonderland.

following are some basic metrics used for this task: 1.


The trade-off is that, we have to maintain a good enough local lexicon yet with extremely limited number of entries.

the training data is a 2.5tB Japanese Web page set.


Since all the probabilities are estimated in a maximum likelihood way, we can simply update the probabilities in these models by accumu-lating frequencies to similar words/phrases.

furthermore, by connecting several Chinese characters together, the number of yielded words and frequently used phrases/idioms are in million level.


We argue it is essential for the IME system to regularly update its compound lexicon to cover these new and hot words/phrases.

how to choice the context from largescale Web pages such that the context is optimized to be used in a mobile device oriented Japanese IME?


In our corpus, we found that 96.6% of the total interactions are Linear Disjoint interactions.

in such an interaction, user interacts with the system with a topic in mind and a goal to achieve.


A relevant work in this direction is by Mejer and Crammer (2012).

it is not extendable to edge correctness prediction.


We have shown that our predictive model can select a source language -based on only monolingual features of the source and target languages -that improves tagger accuracy compared to choosing the single, best-overall source language.

creating annotated linguistic resources is expensive and time-consuming.


4 summarizes the gazette sizes along with precision for NE types from both agriculture and mechanical engineering (IC engine parts) corpora.

utility and effectiveness of our method is evident from its ability to discover new named entities.


The definition of M ED0 is extended to use a given set W (rather than a single word w) by taking the average of the M ED0 distance between g and each word in W : m We assume that a subroutine M ED(D, K, W, g) returns the MED distance M ED D,K (g, W ), as defined above.

createGazetteM ED clearly outperforms BASILISK for all other categories.


All these research have showed us the very good capability of the neural language model in learning word representation.

as input, character features (unigram or bigram features) are fed as indices taken from a finite dictionary D. This dictionary D contains all the character features which appeared in the training data.


The greater α is, the probability of producing more classes increases.

the details of our model are elaborated in Section 3.


So far we have extended the potential function used in node cliques of a CRF to a non-linear DNN.

overall we found that the feature set we used is competitive with CRF results from earlier literature (Turian et al., 2010;Collobert et al., 2011).


The task of this study is to predict the category of each article from its content (text).

one is (e) all data in 2005 newspapers are used for adaptation (Normal Case).


Methods We test the methods described in Sections 2.2 and 2.3 (represented as 'Transfer' and 'Online,' respectively).

if the documents that users want to apply the systems to do not belong to the domain of the annotated corpora, the resulting accuracy tends to be unsatisfactory.


Our future work is to apply model adaptation to structured learning.

it usually loses information about old samples (in our case, the original data).


Choosing a suitable set of features and automatically obtaining values for features pose obstacles for these methods (Islam and Inkpen, 2008).

the human-and computer-generated scores are compared; the results are promising, but point to the need for further refinement.


We applied the algorithm to 15 pairs of sentences written for the purpose of testing the approach.

they achieve improved results by examining word pairs instead of single words (Okazaki et al., 2003).


Here, patterns are generated as a sequence of typed dependencies and lemmas of tokens on the shortest path between two entities in a parse.

firstly, it does not necessarily produce patterns at the desired granularity.


We therefore implement this option mainly for evaluation purposes, in order to determine URE capabilities given a fine-grained, high quality type system for selectional restrictions.

unsupervised Relation Extraction (uRE) approaches do not require target relations to be pre-specified, and require no labeled training data (Rosenfeld and Feldman, 2007).


In an analysis of the prevalence of coordinations in different corpora, we observed that long coordinations (those with at least three conjuncts) are more prevalent in patents than in other genres (average length of 4.6 in EPO vs 3.6 in other corpora).

we show that the restriction to coordinations has several additional benefits, such as improved extraction of multiword expressions, and the possibility to scale up previous efforts.


Such trees differ in the position of the EVENT/RELATION nodes (at level 1 of the tree).

it should be noted that the above results do not indicate the final system accuracy: for this purpose, the next section shows the 3-fold crossvalidation results using cost-fact values that are (i) derived from the validation set (not included in the cross-validation data) and (ii) slightly different from those optimizing the plot in the figures.


We also show that exploiting syntactic information of the sentence containing the relational expressions is not trivial.

results on two data sets-Machine reading and TimeBank-with 3-fold crossvalidation show that the combination of traditional feature vectors and the new structural features improves on the state of the art for inter-sentence TErE by about 20%, achieving a 30.2 F1 score on intersentence TErE alone, and 47.2 F1 for all TErE (inter and intra sentence combined).


the evaluation results are shown in table 7.


The purity score is close to perfect, which means that the merged expressions are mostly regarded as synonyms.

especially, the precision of the extraction models is improved by 7.9%.


model is used to structure unstructured pages in the category.

we obtain a vector of attributes that can be regarded as synonyms, such as (region, country), and (grape, grape variety) for the wine category.


An evaluation was conducted for each component, that is, evaluation of the KB (Section 5.2), evaluation of the automatically annotated corpora (Section 5.3), and evaluation of the extraction models (Section 5.4).

we employ a simple rule-based approach to induce the KBs.


This is because LMUC uses w sing to penalize these four responses for identifying wrong singleton clusters.

case 3: |c i j | = 1 In this case, K i and S j have one mention in common.


Despite the large differences in these responses, B 3 only gives 0.7% more points to response (e) than response (a).

here, response (e) is assigned a higher score by LCEAF(W 3 ) than response (a): response (a) is heavily penalized because of the many erroneous clusters it contains.


As expected, LMetrics(W 4 ) and LMetrics(W 5 ) assign much lower scores to response (a) than the OMetrics, owing to a relatively small value of w pro .

w L c returns w sing if all of C i j , K i and S j contain exactly one mention (which implies that the singleton cluster C i j is correctly identified); otherwise, w L c returns 0.


One of MUC's shortcoming is that it fails to reward successful identification of singleton clusters.

the same is no longer true in a linguistically aware setting: since the links may not necessarily have the same weight, the weight assigned to C i j depends on which |C i j | − 1 links are chosen.


We, then evaluated the performance of these corpora on the Libya dataset.

at the end of this step we have list of documents (denoted by D lm ) in which both the words co-occur along with their frequency in the documents.


Wikipedia Simple is a relatively new version of the Wikipedia English, where articles are written in simple English 7 .

the Flesch-Kincaid formula (Flesch, 1948;Kincaid et al., 1975) was probably the first in gaining wide recognition among publishers.


MAP(ˆ | r 0 ), where we want to obtain the most plausible linguistic instantiationˆ given a certain readability level r 0 .

content producers might be tempted to adapt their manuscripts by tweaking the text features present in readability formulas, without gaining (or even degrading) real readability (Davison and Kantor, 1982).


needs to be tagged as containing a cause relation.

to find the most prevalent discourse relations for opinion summarization, we have used the tAC 2008 opinion summarization track input document set (collection) which is a subset of BLOG06 and the answer nuggets provided by tAC 2008 as the reference summary (or model summaries), which had been created to evaluate participants' summaries at the tAC 2008 opinion summarization track.


As a baseline, we used the original candidate sentences generated by MEAD.

we have complemented this parser with three other approaches: (Jindal and Liu, 2006)'s approach is used to identify intra-sentence comparison relations; we have designed a tagger based on (Fei et al., 2008)'s approach to identify topicopinion relations; and we have proposed a new approach to tag attributive relations (Mithun, 2012).


(2008) and have produced better human agreement in annotation experiments than generic sentence fusion (Daumé III and Marcu, 2004).

9 We therefore use two baselines for this evaluation.


There are hundreds of particles in the Korean language, but many of these are not used often, e.g., 9 particles cover 70% of particle use in a data set of thesis abstracts and 32 cover 95% in a study by Kang (2002).

due to the somewhat ambiguous nature of particles with respect to other morphemes and root endings, we cannot be certain that all of these edits are in fact particles, but can be confident that a majority are.


All particles (erroneous or correct) are labeled as to their function (e.g., locative), allowing us to group particles into categories, to see how classifier performance differs.

this classifier can provide useful feedback to learners, especially higher-level ones who may know the correct particle once its omission is highlighted.


Lexical error (lex) shows 69% precision and 66% Fmeasure.

czech is a West Slavic language that belongs to the Indo-European language family.


(2005) report slightly above 80% accuracy (with all features combined) compared to 20% baseline for 5-class classification.

at the same time, for other errors, it is impossible to identify their nature and group prevalence based on the data available.


If an essay was written reasonably well the participants assumed that the author belonged to the IE group of learners, and vice versa.

we observe that some of the best performing errors occur when a learner's L1 interferes and affects the production of L2.


Further, we would like to perform experiments with larger sets of data and to compare the performance of features for other levels of proficiency.

the results provide empirical evidence for the different types of errors discussed within the Error Analysis approach.


This reduces the number of required dot-products into the sum of the number of classes and the words in a class.

the number of target words must be limited.


Our experiments on the WMT'14 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique.

on the target side, we insert a positional token p d after every word.


Previous work on VDR-based image description has relied on training data from expert human annotators, which is expensive and difficult to scale to other data sets.

our approach is to find the objects mentioned in a given description using a state-of-the-art object detector, and to use successful detections to produce training data.


This could be attributed to the shift in the types of scenes depicted in each data set.

it contains a wide variety of subject matter drawn from the original 20 PASCAL Detection classes.


QA is another representative sentence matching problem.

if we map the representations of TEXTCHUNK S 1 into an appropriate space then we can hope that similarity between these transformed representations of S 1 and the representations of TEXTCHUNK S 2 do yield useful features.


(2013) propose a framework for taxonomically categorizing forum posts, leveraging manual annotations.

we find that around 90% of MOOC posts have only one aspect, which makes sentence-level aspect modeling inappropriate for our domain.


We use the maximum value in the posterior for the distribution over topics for each post to obtain predictions for coarse aspect, fine aspect, and sentiment.

using the top three predictions made by PSL-Joint, we can understand the fine aspect of the post to a great extent.


We provide results only on WS-353 and SCWS, since the above-mentioned approaches do not report their performance on other datasets.

the task provides a dataset comprising 79 graded word relations, 10 of which are used for training and the rest for test.


(2012) applied K-means clustering to decompose word embeddings into multiple prototypes, each denoting a distinct meaning of the target word.

in order to alleviate this issue, we leveraged a state-of-the-art Word Sense Disambiguation (WSD) algorithm to automatically generate large amounts of sense-annotated corpora.


As representatives for graph-based similarity techniques, we report results for the state-of-theart approach of Pilehvar et al.

these techniques either suffer from low coverage as they can only model word senses that occur in the parallel data, or require manual intervention for linking the obtained representations to an existing sense inventory.


Figure 3 draws the envelope of histogram of cosine distance between all target-choice word pairs in the GRE test set, calculated in the embedding space learned with MCE.

we use antonym pairs in lexical resources to learn contrasting neighbors.


Each GRE question has a target word and five candidate choices; the task is to identify among the choices the most contrasting word with regard to the given target word.

by definition, opposites are a subset of contrasting word pairs (refer to (Mohammad et al., 2013) for detailed discussions).


It can be seen as the intrinsic dimensionality of a latent embedding of the elementary feature vectors.

a standard approach to control the capacity of a linear classifier is to use 1 or 2 regularization on the parameter vector.


Therefore, syntagmatic models tend to favor the use of larger text regions as context.

the second category mainly captures paradigmatic relations, which relate words that occur with similar contexts but may not cooccur in the text.


A low-rank decomposition is then conducted to learn the distributional word representations.

the objective function of PV-DM can no longer decomposed as the PDC model as shown in Equation 4and (5).


The parameters of our models are learned using AdaGrad (Duchi et al., 2011) with`2 regularization via regularized dual averaging (Xiao, 2009), and we used random search on the development set to select hyperparameters.

an alternative and related approach to learning template orderings is based on the Group Orthogonal Matching Pursuit (GOMP) algorithm for generalized linear models (Swirszcz et al., 2009;Lozano et al., 2011), with a few modifications for the setting of high-dimensional, sparse NLP data (described in appendix B).


This work was supported by the National Basic Research Program of China (No.

the convolution layer aims to capture the compositional semantics of a entire sentence and compress these valuable semantics into feature maps.


The semantic interactions between the predicted trigger words and argument candidates are crucial for argument classification.

cNNs typically use a max-pooling layer, which applies a max operation over the representation of an entire sentence to capture the most useful information.


In S1, beats is a trigger of type Elect.

in S1, beats is a trigger of type Elect.


These methods yield relatively high performance.

we call these knowledge lexical-level clues.


It proves that richer feature sets lead to better performance when using traditional human-designed features.

we proposed the PF, which is defined as the relative distance of the current word to the predicted trigger or candidate argument.


In our algorithm, we only use the filler provenance for a given slot fill.

when combining the output of different ESF systems, it is possible that some slot-filler entities might overlap with each other.


The RPI BLENDER KBP system (Yu et al., 2014) casts SFV in this framework, using a graph propagation method that modeled the credibility of systems, sources, and response values.

section 3 provides general background on the KBP-EsF task.


Using the union of these keys as the gold standard, precision, recall, and F1 scores are computed.

stacking (Sigletos et al., 2005;Wolpert, 1992) has not previously been employed for ensembling KBP-ESF systems.


Document classification is expected to reduce false positives in irrelevant documents while not dramatically reducing recall.

this loss is not significant and the overall Fscore finally increases by 5%.


Simultaneous translation (Section 2) avoids this problem by starting to translate before observing the whole sentence, as shown in Figure 1 (a).

sentence segmentation methods have the obvious advantage of allowing for translation as soon as a segment is decided.


However, as translation starts before the whole sentence is observed, translation units are often not syntactically or semantically complete, and the performance may suffer accordingly.

in addition, we hope to expand the methods proposed here to a more incremental setting, where both parsing and decoding are performed incrementally, and the information from these processes can be reflected in the decision of segmentation boundaries.


However, this trend is not entirely unexpected because it is not possible to completely accurately guess syntactic constituents from every substring w. For example, parts of the sentence "in the next 18 minutes" can generate the sequence "in the next CD NN " and " IN DT JJ 18 minutes," but the constituents CD in the former case and DT and JJ in the latter case are not necessary in all situations.

parsing is performed by finding the parse tree T that maximizes the pCFG probability given a sequence of words w ≡ [w 1 , w 2 , • • • , w n ] as shown by Eq.


In incremental decoding, each incoming word is fed into the decoder one-by-one, and the decoder updates the search graph with the new words and decides whether it should begin translation.

short segments pose problems for syntaxbased translation methods, as it is difficult to generate accurate parse trees for sub-sentential segments.


One of the most identifying features of speech translation is the fact that it must be performed in real time while the speaker is speaking, and thus it is necessary to split a constant stream of words into translatable segments before starting the translation process.

this trend is not entirely unexpected because it is not possible to completely accurately guess syntactic constituents from every substring w. For example, parts of the sentence "in the next 18 minutes" can generate the sequence "in the next CD NN " and " IN DT JJ 18 minutes," but the constituents CD in the former case and DT and JJ in the latter case are not necessary in all situations.


This is perhaps because the RTE community has almost entirely focused on single sentence text hypothesis pairs for a long time.

we will explore a few definitions of sub-tasks in our experiments.


We detect negation (either in the hypothesis or a sentence in the text snippet aligned to it) using a small set of manually defined rules that test for presence of words such as "not", "n't", etc.

we can use the same setup as before for multi-task learning after appropriately changing the feature map.


Nevertheless, since most the existing works learned word representations mainly based on the word co-occurrence information, the obtained word embeddings cannot capture the relationship between two syntactically or semantically similar words if either of them yields very little context information.

can be optimized using back propagation neural networks.


Through the two steps, the proposed approach can map a question into a length invariable compact vector, which can be efficiently and effectively for large-scale question retrieval task in cQA.

the training time yields linear scale to the number of noise samples and it becomes independent of the vocabulary size.


These methods retrieve a set of candidate answers from the knowledge base, and the extract features for the question and these candidates to rank them.

as shown in Figure 1, the 2-hops path between the entity avatar and the correct answer is (film.film.release date s, film.film regional release date.release date).


Both chimera and the intruder methods are flexible, and we plan to explore them further in future research.

we focus on translating from English to Italian and adopt the setup (word vectors, training and test data) of Dinu et al.


1 Although such studies comparing similarity judgements have their merits, it would be interesting to have studies that evaluate methods for composition on a larger scale, using a larger test set of different specific compositions.

in fact: • A non-reduced space contains more information.


This might be beneficial for methods that are able to take advantage of the full semantic space (viz.


The interaction of the parameters and the nonlinearity also makes the objective nonconvex.

our combined sparse and neural model trains on the Penn Treebank in 24 hours on a single machine with a parallelized CPU implementation.


We then discuss specific choices of our featurization (Section 2.3) and the backbone grammar used for structured inference (Section 2.4).

table 1 shows that these features provide no benefit to the baseline model, which suggests either that it is difficult to learn reliable weights for these as sparse features or that different regularities are being captured by the word embeddings.


With regard to parsing speed, 1-order-atomic is the fastest while other two models have similar speeds as MSTParser.

for model training, we use the Max-Margin criterion.


Given a sentence x, graph-based models formulates the parsing process as a searching problem: where y * (x) is tree with highest score, Y (x) is the set of all trees compatible with x, θ are model parameters and Score(x,ŷ(x); θ) represents how likely that a particular treeŷ(x) is the correct analysis for x.

table 3 lists the UAS of three models on development set.


The effectiveness of our neural network depends on five key components: Feature Embeddings, Phrase Embeddings, Direction-specific transformation, Learning Feature Combinations and Max-Margin Training.

the effectiveness of this function relies heavily on the design of feature vector f (x, c).


Each point represents maximization over a small hyperparameter grid with early stopping based on WSJ tune set UAS score.

this increases POS  Bohnet and Kuhn (2012) 93.27 91.19 40 Chen and Manning (2014) 91.80 89.60 1 S-LStM (Dyer et al., 2015) table 1: Final WSJ test set results.


Such an extension of the work would make it an alternative to architectures that have an explicit external memory such as neural Turing machines (Graves et al., 2014) and memory networks (Weston et al., 2015).

structured objects such as sequences of discrete symbols are written with lowercase, bold, italic letters (e.g., w refers to a sequence of input words).


We show that our approach performs well on one such downstream application: the KBP Slot Filling task.

the most salient features are the label of the edge being taken, the incoming edge to the parent of the edge being taken, neighboring edges for both the parent and child of the edge, and the part of speech tag of the endpoints of the edge.


2 For example, removing the amod edge in cute amod ← −− − rabbit yields the more general lexical item rabbit.

for both of the other actions, it is often the case that we would like to capture a controller in the higher clause.


However, if "with" is used in the sense of "accompanied by", then the PP is a likely verb attachment, as in the quad visted, P aris, with, Sue.

similarly, suppose we know that net-caught-butterfly, svo (n2, v, n1).


In addition to prior work on prepositional phrase attachment, a highly related problem is preposition sense disambiguation (Hovy et al., 2011;Srikumar and Roth, 2013).

our approach draws upon diverse sources of background knowledge, leading to performance improvements.


Notice that when we used a filtered version of the data, in feature F 2, the data was no longer detrimental to performance.

even a syntactically correctly attached PP can still be semantically ambiguous with respect to questions of machine reading such as where, when, and why.


In our experiments we used both kinds of models, but found the discriminative model performed better.

for the WKP & NYTC corpora, each quad has a preceding noun, n0, as context, resulting in PP 5-tuples of the form: {n0, v, n1, p, n2}.


We also used different types of noun categorizations: WordNet classes, semantic types from the NELL knowledge base (Mitchell et al., 2015) and unsupervised types.

we present details only for our discriminative model.


Finally, we use lexical features in the form of PP quads, features F8-15.

pps are a major source of syntactic ambiguity.


Both support comparisons, and ideally we can detect some level of similarity.

while SNK may have an inherent advantage over CSR or Bow due to its entity orientation, to investigate the effectiveness of the method itself, we now compare them on the previous task of comparative sentence identification.


The most accurate parsers (ClearNLP, Mate, RBG, Redshift, Turbo, and Yara) separate from the remaining when sentence length is more than 20 tokens.

we analyzed parser accuracy by sentence length in bins of length 10 (Figure 4).


A approach was proposed in (Van der Plas et al., 2014) in which information is aggregated at the corpus level, resulting in a significantly better SRL corpus for French.

furthermore, we have applied our approach to generate PropBanks for 7 languages and conducted experiments that indicate a high f 1 measure for all languages (Section 4).


We propose filtered projection focused specifically on improving the precision of projected labels.

sRL models produced in this set of experiments were evaluated using French gold , sampled and evaluated in the same way as other experiments in this section for comparability.


We observe that the BITEXT approach outperformed the SUPERVISED and the DELEXICALIZED ones in all metrics with a considerable margin, which shows the effectiveness of our proposed method.

obtain the best target word and its polarity, ( t, p) := arg max t,p f o→t (x, TARGET:p); 3.


Query-based multilingual opinion mining was addressed in several NTCIR shared tasks (Seki et al., 2007;Seki et al., 2010).

the application of this simple approach to the gold dependency graphs in the training partition of the MPQA leads to oracle F 1 scores of 86.0%, 95.8% and 93.0% in the reconstruction of opinion, agent and target spans, respectively, according to the proportional scores described in §5.2.


The task-specific features are designed to train sentiment polarity classifiers.


In the literature of CSLA, the language with abundant reliable resources is called the source language (e.g., English), while the low-resource language is referred to as the target language (e.g., Chinese).

p: precision, R: Recall, F1: micro-F measure, Ac: Accuracy, and -represents unknown.


The extensive research and development efforts produce a variety of reliable sentiment resources for English, one of the most popular language in the world.

the parallel data is also a scarce resource.


This paper proposes an approach to learning B-SWE by incorporating sentiment information into the bilingual embeddings for CLSC.

we take into account 14 frequently-used negation words in English such as not and none; 5 negation words in Chinese such as Ø (no/not) and vk (without).


Directly employing the translated resources for sentiment classification in the target language is simple and could get acceptable results.

the process of supervised phase is shown in Figure 2.


Parameter l } is learned by maximizing the objective function according to the sentiment polarity label s i of document d i : Through the supervised learning phase, [W E , W C ] is optimized by maximizing sentiment polarity probability.

bilingual embeddings without sentiment information are not effective enough for sentiment classification task.


This can be formalized with the following equation for the hub score of a node: Where h(v) is the hub score for node v, successors(v) is the set of all nodes that v has an edge to, and a(u) is the authority score for node u.


We also want to experiment with removing the dependency on the Treex surface realizer by generating directly into dependency trees or structures into which de- This work was funded by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221 and core research funding, SVV project 260 104, and GAUK grant 2058214 of Charles University in Prague.

we introduced a novel modification of the perceptron updates to improve scoring of incomplete sentence plans: In addition to updating the weights using the top-scoring candidate t top and the gold-standard tree t gold (see above), we also use their differing subtrees t i top , t i gold for additional updates.


The resulting model will have parameters for feature types observed in the source domain as well as the target domain.

y n be the original representations of the associated words set contained in the labels.


(2011) increases the F1 score slightly from 75.13 to 75.69 in C2F, but it does not help as much in cases that require bijective mapping: Daume, Union and Pretrain.

v k ∈ R d can be used to project the variables from the original d-and d -dimensional spaces to a k-dimensional space: The new k-dimensional representation of each variable now contains information about the other variable.


Nevertheless, combining two relations (column (f)) outperforms both results for ASR and manual transcripts, showing that different types of relations can compensate each other and then benefit the SLU performance.

some important contexts may be missing due to smaller windows, while larger windows capture broad topical content.


After replacing original bag-of-words contexts with dependencybased contexts, we can train dependency-based embeddings for all target words (Yih et al., 2014;Bordes et al., 2011;Bordes et al., 2013).

f w carries the basic word vectors for the utterances, which is illustrated as the left part of the matrix in figure 1(b).


We annotate about 25k spoken sentences with only disfluency annotations according to the guideline proposed by Meteer et al.

in comparing parsing accuracy, our BCT model outperforms all the other models, showing that this model is more robust on disfluent parsing.


our novel right-to-left transition-based joint method caters to the disfluency constraint which can not only overcome the decoding deficiency in previous work but also achieve significantly higher performance on disfluency detection as well as dependency parsing.


Instead of defining a more complicated structure and learning everything jointly, we employ a two-stage approach as the solution for modeling entity-entity relationships after we found that S-MART achieves high precision and reasonable recall.

first, we consider two linear structured learning algorithms: Structured Perceptron (Collins, 2002) and Linear Structured SVM (SSVM) (Tsochantaridis et al., 2004).


However, most existing approaches takes two extreme ways: either extract relations based on pre-defined ontology, such as DBpedia (Lehmann et al., 2014); or cluster relation without referring to some ontology, such as OpenIE (Etzioni et al., 2011).

the set of factoids F plus the set of decision variables y are the random variables in our factor graph model.


Semantic search also targets on returning answers directly (Pound et al., 2010;Blanco et al., 2011;Tonon et al., 2012;Kahng and Lee, 2012).

a similar factor graph model has been proposed to solve the coreference resolution in (Singh et al., 2011;Wick et al., 2012).


On one hand, an entity is modeled to have multiple internal representations, each regarding one or more closely related facts.


However, with the further increase of the threshold, we introduce more noise, which decreases the performance.

this shows that introducing better quality of background knowledge is helpful to the better classification of documents.


They first proposed a search enginebased method to evaluate the relatedness between every pair of triples, and then an iterative propagation algorithm was introduced to select the most relevant triples to a given source document (see Section 2), which achieved a good performance.

in the original representation, there are no edges between two bk-nodes because they treat the bk-nodes as recipients of relevance weight only.


When evaluating the top 10 triples with the highest relevance weight, our framework outperforms the best baseline LDA by 4.4% in MAP and by 3.91% in P@N. When evaluating the top 5 triples, our framework performs even better and significantly outperforms the best baseline by 5.87% in MAP and by 17.21% in P@N. To analyze the results further, Ours-S, the simplified version of our model without iterative propagation, outperforms two strong baselines VSM and WE, which indicates the effectiveness of encoding distributional semantics.

the most closely related work in this area is our own (Zhang et al., 2014), which used the triples of SPO as background knowledge.


We first focus on the impact of decreasing the relevance weight of bk-nodes and increasing that of sd-nodes after every iteration.

one can expect that this representation is helpful for better document enrichment by incorporating both accuracy and coverage.


what really matters are not the raw cost amounts, which may be very small, but rather the relative cost of looking up an attribute compared to that of receiving a follow-up.


We believe that an essay that contains too many of these component-less paragraphs is likely to have taken too much space discussing issues that are not relevant to the main argument of the essay.

to gain insight into how much impact each of the feature types has on our system, we perform feature ablation experiments in which we remove the feature types from our system one-by-one.


's method to tag each sentence of our essays with an argument label, but modify their method to accommodate differences between their and our corpus.

if more than one of these rules applies to a sentence, we tag it with the label from the earliest rule that applies.


Our approach consists of two core components-a timeaware hierarchical Bayesian model for event detection, and a learning-to-rank model to select the salient events to construct the final chronicle.

a sports chronicle should provide information about the results of semi-final and final, and the champion of the tournament instead of the first-round match's result, which accounts for the poor performance.


Through formulating this task as a binary classifi-cation problem, we adopt the Support Vector Machines (SVMs) (Cortes and Vapnik, 1995) as the learning model.

morphs can be very abstract (e.g., "函 数 (Function)" refers to "杨 幂 (Yang Mi)" because her first name "幂 (Mi)" means the Power Function) or very concrete (e.g., "薄督 (Governor Bo)" refers to "薄熙来 (Bo Xilai)").


Table 1 contains a list of all datasets.

we formulate the disambiguation as a continuous, multi-objective optimization problem.


Unfortunately, continuous expansion will soon render a paper unreadable (e.g., one of many extensions to Polymerase Chain Reaction is Standard Curve Quantitative Competitive Reverse Transcription Polymerase Chain Reaction).

more importantly, the majority of phrases that are never abbreviated are simply not Computer Science keyphrases (we return to this in Section 4.6).


The algorithm then extracts a "window" of text preceding the parenthesis, up to n words long (where n is the character length of the abbreviation plus padding).

for example, if the text contains the phrase, ".


It is worthwhile to mention that the priors are not a compulsory component.

for the JST and the Trying-JST methods only, we use the filtered subjectivity lexicon (subjective MR) as prior information, containing 374 positive and 675 negative entries, which is the same experimental setting as in Lin & He (2009).


Suppose that the training set is where n s is the number of training objects.

the procedure for obtaining priors is generic and can eas-ily be applied to any given dataset.


This is an advantage over softmax classifiers.

the nine relations are Cause-Effect, Component-Whole, Content-Container, Entity-Destination, Entity-Origin, Instrument-Agency, Member-Collection, Message-topic and Product-Producer.


If a trigram appears as the largest contributor for more than one sentence, its contribuition value becomes the sum of its contribution for each sentence.

when using only the text span between the target nouns, the impact of WPE is much smaller.


It is unclear at the first glance how to encode word embeddings into the tree kernels effectively so that word embeddings could help to improve the generalization performance of RE.

we report the performance of these augmented systems in Table 2 for the two scenarios: (i) in-domain: both training and testing are performed on the source domain via 5-fold cross validation and (ii) out-of-domain: models are trained on the source domain but evaluated on the three target domains.


The closest phrase of X1 in the source domain is X3: the phrase between "Iraqi soldiers" and "herself" in the sentence "The Washington Post is reporting she shot several Iraqi soldiers before she was captured and she was shot herself , too.".

in general, suppose we are able to acquire an additional real-valued vector V i from word embeddings to semantically represent a relation mention R i (along with the PET tree T i ), leading to the new representation of The new kernel function in this case is then defined by: is some standard vector kernel like the polynomial kernels.


note that it does not mean that the selected reference point should have exactly same surface form across time.


Eq.1 is used for solving regularized least squares problem (γ equals to 0.02).

we call this kind of similarity matching as local correspondence in contrast to global correspondence described in Sec.


To achieve this, we propose using backward random walks.

knowledge bases such as Freebase (Bollacker et al., 2008), YAGO (Suchanek et al., 2007), or NELL (Carlson et al., 2010a), may contain thousands of predicates and millions of concepts.


Such path types may be indicative of an extended relational meaning between graph nodes that are linked over these paths; for example, the path AtheletePlaysForTeam, TeamPlaysInLeague implies the relationship "the league a certain player plays for".

pRA is highly scalable compared with other statistical relational learning approaches, and can therefore be applied to perform inference in large knowledge bases (KBs).


We represent part-of-speech tags as another set of graph nodes, where word mentions are connected to the relevant tag over P OS edge type.

the relational paths that connect nodes c and t are evaluated as possible random walk features.


Here we want to mention a caveat: there are definitely other common sense Axioms that we are not able to address in the current implementation.

since the visual recognition is not the core of this work, we omit the details here and refer the interested reader to (Aksoy et al., 2014;Aksoy and Wörgötter, 2015).


Triplets are represented where h i denotes a head entity, t i denotes a tail entity and r i denotes a relation.

2014) proposes an improved model named translation on a hyperplane.


Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance.

recently, a powerful approach for this task is to encode every element (entities and relations) of a knowledge graph into a low-dimensional embedding vector space.


(2014) to compare each team's performance on different error types in the CoNLL-2014 shared task.

before commencing annotation, however, each annotator was given detailed instructions on how to use the tool, along with an explanation of each of the error categories.


1 On the one hand, the increasing amount of ontologies offers an excellent opportunity to link this knowledge together (Gómez-Pérez et al., 2013).

the Fill-Up model has been developed to address a common scenario where a large generic background model exists, and only a small quantity of domain-specific data can be used to build a translation model.


Moreover, supervised approaches tend to be such that they can disambiguate only those words for which they have seen sufficient training examples to cover all senses.

we therefore find the mapping between elements of the two pairs that gives the lowest total distance, and halve it: with this method we observe a Krippendorff's α of 0.777; this is only slightly below the 0.8 threshold recommended by Krippendorff, and far higher than what has been reported in other sense annotation studies Jurgens and Klapaftis, 2013).


Differences in data pre-processing (tokenization/lemmatization), selection (train/test splits), feature representation (unigram/bigram), pivot selection (MI/frequency), and the binary classification algorithms used to train the final classifier make it difficult to directly compare results published in prior work.

vertical bars represent the classification accuracies (i.e.


(2007), but not part of the train/test domains.

criteria (b) captures the prior knowledge that high-frequent words common to two domains often represent domain-independent semantics.


For Word2Vec and PMI-SVD, we use the pre-trained models obtained by Baroni et al.

instead of simply backing off to the most frequent sense, we propose a more meaningful exploitation of this information.


This enables the use of natural language processing techniques that require the reliable identification of words.

we learn a GAM using the residuals of the baseline model as a response variable and fitting semantic surprisal based on the in-domain model; see Table 2.


In order to restore its status as a probability, Mitchell includes another normalization step: The model hence simply uses the trigram model probability for function words, making the assumption that the distributional representation of such words does not include useful information.

this means that the simpler trigram surprisal model does not contribute anything over the semantic model, and that the semantic model fits the word duration data better.


We make use of a re-implementation of the semantic surprisal model presented in Mitchell et al.

we decided to divide the data up into datapoints with S Semantics above 1.5 and below 1.5.


Both SBTDM methods have runtimes that increase at a rate substantially below that of the square root of the number of topics (plotted as a blue line in the figure for reference).


The first, b(a, n), was defined in Section 3.

then, the distribution over a subsequent draw of Z given SBt prior S and observations n is defined as: is a normalizing constant that ensures the distribution sums to one for any fixed number of observations i n i , and B(S, z, n) and Q(z) are defined as below.


Inferring a large topic distribution for each word and document given such sparse data is challenging.

each interior node a of the SBT has a discount δ a associated with it.


We now evaluate the efficiency and accuracy of SBTDM.

the generative model is: The variable φ represents the distribution of topics given words, P (Z|W ).


Training random field models is challenging due to numerical intractability of the normalizing constants Z l (λ) and expectations p λ,l [f ].

the TDRF using the "w+c+ws+cs+cpw" features with class number 200 performs comparable to the RNNLM in both perplexity and WER.


With the experimental setup mentioned before, we want to evaluate performance of this property of our model.

first, n k,d is often a sparse vector, as a document most likely contains only a few of the topics.